Split CIFAR-100 (10 tasks x 10 classes, class-incremental).

Canonical class-incremental continual learning benchmark: CIFAR-100 is split into 10 sequential tasks of 10 classes each. Models learn tasks one at a time without access to prior-task data and are evaluated on average accuracy across all tasks after the full sequence.

Best published scores.

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Primary

average_accuracy · higher is better

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

• 01A public checkpoint or API endpoint
• 02A reproduction script with frozen commit + seed
• 03Declared evaluation environment (Python, deps)
• 04One row per metric declared by this dataset
• 05A contact so we can follow up on discrepancies